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Gold Science and Technology ›› 2023, Vol. 31 ›› Issue (3): 487-496.doi: 10.11872/j.issn.1005-2518.2023.03.153

• Mining Technology and Mine Management • Previous Articles     Next Articles

Quality Evaluation of Tunnel Surrounding Rock in Karst Area Based on Comprehensive Weight-Fuzzy Matter-element Method

Weizhong ZHANG(),Wei YUAN,Qinrong KANG(),Yuandi XIA,Mengling LI   

  1. School of Resources and Safety Engineering,Wuhan Institude of Technology,Wuhan 430073,Hubei,China
  • Received:2022-10-21 Revised:2023-02-14 Online:2023-06-30 Published:2023-07-20
  • Contact: Qinrong KANG E-mail:wzzhang1120@126.com;kang801118@163.com

Abstract:

The distribution area of karst in China can reach about 33% of the country’s land area,therefore,in the process of tunnel excavation and construction in karst area,it is inevitable to cross the karst development location.At the same time,due to the hidden nature and irregularity of karst development,water and mud surges may occur locally in tunnel excavation,which greatly reduces the safety and stability of the tunnel and easily causes collapse accidents and threatens construction safety.To solve this problem,the geological situation of the surrounding rock needs to be fully grasped.Therefore,the evaluation of the quality of the surrounding rock is of great significance to the safety of tunnel rock design and construction.In order to scientifically and accurately evaluate the surrounding rock quality of karst tunnels,CRITIC method was used to determine the objective weight of the surrounding rock quality index affecting the tunnel in karst areas according to the measured data.The improved analytic hierarchy process was used to determine the subjective weight,and the combined weight was used to calculate the comprehensive weight of each index.Finally,the classification of surrounding rock was determined according to the fuzzy matter-element theory.Thus,the evaluation method of surrounding rock quality of tunnel in karst areas based on the comprehensive weight-fuzzy matter-element method was proposed,and this method was applied to the evaluation of surrounding rock quality of tunnel group in the seven-star data center of Tencent in karst areas.The research results show that the comprehensive fuzzy evaluation model established by introducing variance coefficients to the CRITIC method and introducing optimal transfer matrix optimization AHP method can avoid the influence of purely human subjective factors and evaluate and grade the rock quality more objectively and comprehensively.The evaluation results are in good agreement with the actual on-site grading,and the method can achieve a more scientific and accurate comprehensive determination of karst tunnel quality.The rock quality of the tunnel in this project example is mainly Ⅳ and Ⅴ surrounding rocks,which are poor and less stable,and the support of the surrounding rocks need to strengthen.

Key words: tunnel in karst area, quality evaluation of surrounding rock, comprehensive weight, fuzzy matter-element method

CLC Number: 

  • TD853

Fig.1

Calculation step of the improved CRITIC method"

Fig.2

Calculation steps of combined weighting method"

Fig.3

Schematic diagram of fuzzy matter-element evaluation method"

Fig.4

Tunnel layout plan"

Fig.5

Calculation method of KBQ index"

Table 1

Classification standard of surrounding rock classification evaluation index"

一级指标二级指标指标编号稳定性等级
围岩岩体结构特征结构类型X1

整体结构

(0.9~1.0)

整体块状

(0.7~0.9)

层状

(0.5~0.7)

破碎状

(0.3~0.5)

散体状

(~0.3)

岩体RQD/%X290~10075~9050~7525~50<25

岩体单轴抗压强度

/MPa

X3200~300100~20050~10025~500~15
岩体完整性系数X40.75~1.000.55~0.750.30~0.550.15~0.300.00~0.15
节理间距/mX50.8~2.00.3~0.80.2~0.30.1~0.20.00~0.1
围岩地质特征地下水/(L·min-1X6<2525~5050~100100~125125~200
软弱夹层性质X7

无夹层

(0.9~1.0)

软岩、岩块

(0.7~0.9)

岩屑

(0.5~0.7)

岩屑夹泥

(0.3~0.5)

泥夹岩屑、泥质

(~0.3)

岩层倾角/(°)X8<1010~1525~4545~6060~90
KBQX9>550550~451450~351350~211≤210

Table 2

Sample data of rock mass quality evaluation"

工程编号断面位置断面编号指标平均取值
X1X2X3X4X5X6X7X8X9
A1进口S10.252.314.00.420.351020.26232.7
B1进口S20.442.418.40.360.221000.215229.6
出口S30.25242.60.410.181000.315312.6
B2进口S40.245.719.90.380.021050.410156.8
出口S50.255.622.40.430.051080.28173.5
B3进口S60.465.542.90.490.26980.412336.1
出口S70.568.847.40.510.30950.214348.9
B4进口S80.252.334.20.420.101010.212209.4
出口S90.449.030.60.390.141060.415198.3
B5进口S100.242.414.50.360.08960.211166.3
出口S110.442.418.20.350.04940.315167.1
B6起点S120.449.020.40.390.34990.414245.0
终点S130.452.025.60.410.301020.112265.3
油库起点S140.452.021.40.410.29970.413249.0
终点S150.265.514.50.480.241000.310248.9

Table 3

Objective weight of each evaluation index"

指标编号权重指标编号权重
X10.141X60.025
X20.055X70.175
X30.148X80.105
X40.040X90.079
X50.232

Table 4

Subjective weight of each evaluation index"

指标编号权重ω指标编号权重ω
X10.230X60.076
X20.049X70.148
X30.061X80.095
X40.185X90.118
X50.039

Table 5

Calculation results of comprehensive weight"

指标编号因素主观权重客观权重αβ综合权重
X1结构类型0.2300.1410.5940.4060.194
X2岩体RQD0.0490.0550.051
X3岩体单轴抗压强度0.0610.1480.096
X4岩体完整性系数0.1850.0400.126
X5节理间距0.0390.2320.117
X6地下水0.0760.0250.055
X7软弱夹层性质0.1480.1750.159
X8岩层倾角0.0950.1050.099
X方正汇总行9KBQ0.1180.0790.102

Table 6

Classification results of tunnel surrounding rock"

工程编号(名称)断面位置断面编号围岩贴进度围岩评价等级

实测

等级

A1进口S10.296
B1进口S20.315
出口S30.321
B2进口S40.285

出口S50.269
B3进口S60.404
出口S70.395
B4进口S80.282
出口S90.313
B5进口S100.256
出口S110.308
B6起点S120.369
终点S130.320
油库起点S140.370
终点S150.318
Aydan Ö, Ulusay R, Tokashiki N,et al,2014.A new rock mass quality rating system:Rock mass quality rating (RMQR) and its application to the estimation of geomechanical characteristics of rock masses[J].Rock Mechanics and Rock Engineering,47(4):1255-1276.
Cai Wen, Guo Kaizhong, Xu Chulong,et al,1986.Matter element analysis on the investment for the environmental protection project of Guangdong Province[J].Journal of Guangdong Institute of Technology,(2):15-31.
Diakoulaki D, Mavrotas G, Papayannakis L,et al,1995.Determining objective weights in multiple criteria problems:The CRITIC method[J].Computers and Operations Research,22(7):763-770.
Guo S F, Qi S W, Saroglou C,et al,2020.A-BQ a classification system for anisotropic rock mass based on China National Standard[J].Journal of Central South University,27(10):3090-3102.
He P, Wang G, Sun S Q,et al,2020.Reliable stability analysis of surrounding rock for super section tunnel based on digital characteristics of joint information[J].Geomatics,Natural Hazards and Risk,11(1):1528-1541.
Jiang Jie, Pu Ou, Xiaodu Ou,et al,2018.Expert system of surrounding rock classification for highway tunnel in karst region:Design and application[J].Journal of Yangtze River Scientific Research Institute,35(7):94-99.
Li F W, Phoon K K, Du X L,et al,2013.Improved AHP method and its application in risk identification[J].Journal of Construction Engineering and Management,139(3):312-320.
Li Qingbo, Du Pengzhao,2020.Automatic RQD analysis method based on information recognition of borehole images[J].Chinese Journal of Geotechnical Engineering,42(11):2153-2160.
Liu Feiyue, Liu Yihan, Yang Tianhong,et al,2021.Meticulous evaluation of rock mass quality in mine engineering based on machine learning of core photos[J].Chinese Journal of Geotechnical Engineering,43(5):968-974.
Ma Shiwei, Li Shouding, Li Xiao,et al,2020.KNN method for intelligent observational classification of rock mass quality in tunnel[J].Journal of Engineering Geology,28(6):1415-1424.
Mu Chenglin, Huang Runqiu, Pei Xiangjun,et al,2016.Evaluation of rock stability based on combined weighting-unascertained measurement theory[J].Chinese Journal of Geotechnical Engineering,38(6):1057-1063.
Niu Wenlin, Li Tianbin,2015. Optimization of BQ method used in rock mass quality evaluation of rockburst tunnel[J].Journal of Chengdu University of Technology(Natural Science Edition),42(6):658-664.
Wang J C, Guo J,2019.Research on rock mass quality classification based on an improved rough set-cloud model[J].IEEE Access,(7):123710-123724.
Wang Jiaquan, Bai Lei, Lin Zhinan,et al,2020.Analysis of bearing failure characteristics of reinforced soil foundation in karst area[J].Journal of Natural Disasters,29(5):173-181.
Wang Shaoyong, Wu Aixiang, Han Bin,et al,2014.Fuzzy matter-element evaluation of ore-rock cavability in block caving method[J].Chinese Journal of Rock Mechanics and Engineering,33(6):1241-1247.
Wang Wenwen,2019.Research on Regional Ecological Security Evaluation Based on Improved CRITIC-Cloud Model[D].Huainan:Anhui University of Science and Technology.
Wang Xinyi, Yao Mengjie, Zhang Jianguo,et al,2019.Evaluation of water bursting in coal seam floor based on improved AHP and fuzzy variable set theory[J].Journal of Mining and Safety Engineering,36(3):558-565.
Wu Xianguo, Wang Hongtao, He Yun,et al,2018.Excavation stability evaluation of karst tunnel based on fuzzy matter-element[J].China Safety Science Journal,28(1):99-104.
Yin Xin, Liu Quansheng, Wang Xinyu,et al,2020.Prediction model of rockburst intensity classification based on combined weighting and attribute interval recognition theory[J].Journal of China Coal Society,45(11):3772-3780.
Zhang Kai,2012.Study on Method and Application of Surrounding Rock Dynamic Classification of the Karst Tunnel—Taking the Karst Tunnels in Xuda Railway as Example[D].Chengdu:Chengdu University of Technology.
Zhang Q, Jiang Q, Li Y H,et al,2021.Quality evaluation of rock mass using RMR14 based on multi-source data fusion[J].Sensors,21(21):7108-7108.
Zhou Tan, Hu Jianhua, Kuang Ye,et al,2019.Rock mass quality evaluation method and application based on fuzzy RES-multidimensional cloud model[J].The Chinese Journal of Nonferrous Metals,29(8):1771-1780.
蔡文,郭开仲,许楚龙,等,1986.广东环境投资的物元分析[J].广东工学院学报,(2):15-31.
江杰,蒲鸥,欧孝夺,等,2018.岩溶区公路隧道围岩分级专家系统设计与应用[J].长江科学院院报,35(7):94-99.
李清波,杜朋召,2020.基于边缘阈值分割的钻孔图像RQD自动分析方法研究[J].岩土工程学报,42(11):2153-2160.
刘飞跃,刘一汉,杨天鸿,等,2021.基于岩芯图像深度学习的矿山岩体质量精细化评价[J].岩土工程学报,43(5):968-974.
马世伟,李守定,李晓,等,2020.隧道岩体质量智能动态分级KNN方法[J].工程地质学报,28(6):1415-1424.
穆成林,黄润秋,裴向军,等,2016.基于组合赋权—未确知测度理论的围岩稳定性评价[J].岩土工程学报,38(6):1057-1063.
牛文林,李天斌,2015.岩爆隧道岩体质量评价的BQ法优化[J].成都理工大学学报(自然科学版),42(6):658-664.
王家全,柏蕾,林志南,等,2020.岩溶区下伏溶洞加筋土地基承载破坏特性分析[J].自然灾害学报,29(5):173-181.
王少勇,吴爱祥,韩斌,等,2014.自然崩落法矿岩可崩性模糊物元评价方法[J].岩石力学与工程学报,33(6):1241-1247.
王雯雯,2019.基于改进CRITIC-云模型的区域生态安全评价研究[D].淮南:安徽理工大学.
王心义,姚孟杰,张建国,等,2019.基于改进AHP法与模糊可变集理论的煤层底板突水危险性评价[J].采矿与安全工程学报,36(3):558-565.
吴贤国,王洪涛,何云,等,2018.基于模糊物元的岩溶隧道开挖稳定性评价[J].中国安全科学学报,28(1):99-104.
殷欣,刘泉声,王心语,等,2020.基于组合赋权和属性区间识别理论的岩爆烈度分级预测模型[J].煤炭学报,45(11):3772-3780.
张凯,2012.岩溶隧道围岩动态分级方法及应用研究——以叙大铁路岩溶隧道为例[D].成都:成都理工大学.
周坦,胡建华,匡也,等,2019.基于模糊RES-多维云模型的岩体质量评判方法与应用[J].中国有色金属学报,29(8):1771-1780.
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